Data Engineer

About Tekst & Our Mission

At Tekst, we just raised $13.5M in Series A funding to rebuild Process Intelligence for the Agentic era. We're building the context layer that allows AI agents to work at scale on the back-office processes that actually matter. With enterprise customers like Daikin, Colruyt, Mitsubishi and Nokia, we are cutting through the AI hype to deliver real, measurable automation.

The Role

As our first dedicated Data Engineer , you'll build our data infrastructure from zero to one and then own it. Product usage, GTM and finance/contract data all live in separate systems today. You'll bring them together, so Solutions can show a customer their ROI with contract and usage data side by side, and Product, GTM and Finance can work from shared definitions of what "active" or "value delivered" means.

Our product generates real volume and variety of data, which makes this a genuine data engineering challenge: you'll own the architecture end to end - which warehouse or lakehouse (Snowflake, Databricks, or whatever you believe is right - tooling choices are yours to make, in consultation with our CTO), how ingestion and transformation are structured, and how people across the company find and trust the data once it's there.

Once the foundation is live, your focus expands to enabling others: giving Product, GTM and Finance self-serve access to the metrics they need. You'll also run the intake for data/research questions that need real analysis.

What will you be doing?

• Zero-to-one build : Design and build our data platform from scratch, warehouse/lakehouse, ingestion and transformation
• Pipelines that matter : Build and maintain pipelines from the product database, event stream, HubSpot, and finance/contract systems, bringing usage, GTM and contract data into one place.
• Own the metrics, including value and ROI : Define and own our core metrics; product usage as well as the customer value and ROI numbers Solutions needs to prove impact to enterprise accounts, so Product, Sales, CS and Finance work from the same numbers
• Enable teams to self-serve : Build the access and tooling that let Product, GTM and CS answer their own questions directly
• Run the research funnel : Set up a simple intake process for data/research questions from across the company and personally pick up the ones that need real analysis
• Bring people along : Win buy-in from stakeholders across data, product and engineering for shared definitions and self-serve tools, so the platform actually gets used
• Keep it trustworthy : Testing, monitoring, GDPR compliance and cost control as the platform matures from build to maintenance

What's in it for you?

• Competitive salary and equity.
• Real, rare ownership: you decide the architecture and tooling for a data platform built from nothing.
• Your work directly feeds the ROI story Solutions tells enterprise customers like Daikin, Nokia and Colruyt.
• Fun team-buildings, social and sports activities.
• Being surrounded by an all-star team with a unicorn dream and a strong on office culture.

Job requirements

What we're looking for

• Experience : Medior-senior experience in data engineering
• Tech stack : Strong SQL and Python; hands-on with a modern warehouse or lakehouse (Snowflake, Databricks, BigQuery, or similar) and transformation-orchestration tooling (dbt, Airflow, or similar). We're tooling-agnostic - bring your own opinion.
• Data architecture : You think in terms of where data lives, how it flows, and how to make it discoverable and trustworthy for people who aren't data engineers.
• Cross-functional metrics : Experience defining metrics that multiple teams (product, sales, finance) genuinely align on and adopt - this is as much about alignment as it is about SQL.
• Enablement mindset : You get energy from building systems that let other teams move fast on their own.
• Communication & influence : You can win stakeholders across teams over to shared definitions and tools - success here depends on alignment as much as on technical execution.
• Bonus : Experience with BI tooling (Looker, Metabase, Power BI) and using AI tools to speed up analysis.
• Fluent in written and spoken English; Dutch is a plus.